Conceptual and Computational tools to tackle long term risk from nuclear waste disposal in the Geosphere

Project details

Total cost:

EU contribution:

Coordinated in:

Topic(s):

Funding scheme:

CSC - Cost-sharing contracts

Objective

The objective of this SCA is the development of methodology, as well as the realisation of a set of computational tools, for the estimation of long-term risk from nuclear waste disposal in the geosphere. We plan to investigate the groundwater transport of nuclides through a multilayer system in one and two dimensions, and to study the effects of parameters and model assumptions on the predictions of the model. We shall concentrate on those model assumptions which relate to the physico-chemical reactions between the liquid and solid phases. The uncertainties in model parameters and model assumptions will then be investigated by a combination of Monte Carlo methodologies - particularly convenient for computationally expensive models - and Bayesian logic. We plan to use variance-based global sensitivity analysis measures. As for as concep tual model uncertainties are concerned, we propose a full assessment and decomposition of overall uncertainty in which three sources - arising from uncertainty in modelling assumptions (scenario and structural specifications), model parameters, and prediction of observables - are treated together. We propose to combine different sources of uncertainty - including uncertainty in the scenario and structural assumptions of the models themselves - within a coherent Bayesian framework. The basic idea is to integrate over model uncertainty instead of ignoring it or treating it qualitatively. This "global uncertainty and sensitivity" approach, which is new to the field of nuclear fission risk assessment, will bring the results from radionuclide migration investigations closer to what is needed in performance assessment studies and cost-benefit-based decisions. We plan to develop a Monte Carlo driver written for a parallel supercomputer. Considering the large use made of Monte Carlo at all stages of safety studies, and the intrinsically parallel nature of this methodology, it would be extremely useful to have such a parallel MC driver.